RBM20-related cardiomyopathy: current understanding and future options

Splice regulators play an essential role in the transcriptomic diversity of all eukaryotic cell types and organ systems. Recent evidence suggests a contribution of splice-regulatory networks in many diseases, such as cardiomyopathies. Adaptive splice regulators, such as RNA-binding motif protein 20 (RBM20) determine the physiological mRNA landscape formation, and rare variants in the RBM20 gene explain up to 6% of genetic dilated cardiomyopathy (DCM) cases. With ample knowledge from RBM20-deficient mice, rats, swine and induced pluripotent stem cells (iPSCs), the downstream targets and quantitative effects on splicing are now well-defined and the prerequisites for corrective therapeutic approaches are set. This review article highlights some of the recent advances in the field, ranging from aspects of granule formation to 3D genome architectures underlying RBM20-related cardiomyopathy. Promising therapeutic strategies are presented and put into context with the pathophysiological characteristics of RBM20-related diseases

Chromatin-sensitive cryptic promoters putatively drive expression of alternative protein isoforms in yeast

Cryptic transcription is widespread and generates a heterogeneous group of RNA molecules of unknown function. To improve our understanding of cryptic transcription, we investigated their transcription start site (TSS) usage, chromatin organization, and posttranscriptional consequences in Saccharomyces cerevisiae We show that TSSs of chromatin-sensitive internal cryptic transcripts retain comparable features of canonical TSSs in terms of DNA sequence, directionality, and chromatin accessibility. We define the 5' and 3' boundaries of cryptic transcripts and show that, contrary to RNA degradation-sensitive ones, they often overlap with the end of the gene, thereby using the canonical polyadenylation site, and associate to polyribosomes. We show that chromatin-sensitive cryptic transcripts can be recognized by ribosomes and may produce truncated polypeptides from downstream, in-frame start codons. Finally, we confirm the presence of the predicted polypeptides by reanalyzing N-terminal proteomic data sets. Our work suggests that a fraction of chromatin-sensitive internal cryptic promoters initiates the transcription of alternative truncated mRNA isoforms. The expression of these chromatin-sensitive isoforms is conserved from yeast to human, expanding the functional consequences of cryptic transcription and proteome complexity

A privacy-preserving solution for compressed storage and selective retrieval of genomic data

In clinical genomics, the continuous evolution of bioinformatic algorithms and sequencing platforms makes it beneficial to store patients' complete aligned genomic data in addition to variant calls relative to a reference sequence. Due to the large size of human genome sequence data files (varying from 30 GB to 200 GB depending on coverage), two major challenges facing genomics laboratories are the costs of storage and the efficiency of the initial data processing. In addition, privacy of genomic data is becoming an increasingly serious concern, yet no standard data storage solutions exist that enable compression, encryption, and selective retrieval. Here we present a privacy-preserving solution named SECRAM (Selective retrieval on Encrypted and Compressed Reference-oriented Alignment Map) for the secure storage of compressed aligned genomic data. Our solution enables selective retrieval of encrypted data and improves the efficiency of downstream analysis (e.g., variant calling). Compared withBAM, thede factostandard for storing aligned genomic data, SECRAM uses 18%less storage. Compared with CRAM, one of the most compressed nonencrypted formats (using 34% less storage than BAM), SECRAM maintains efficient compression and downstream data processing, while allowing for unprecedented levels of security in genomic data storage. Compared with previous work, the distinguishing features of SECRAM are that (1) it is position-based insteadofread-based,and(2)itallowsrandomqueryingofasubregionfromaBAM-likefileinanencryptedform.Ourmethod thus offers a space-saving, privacy-preserving, and effective solution for the storage of clinical genomic data. © 2016 Szalaj et al

Recommendations for accurate genotyping of SARS-CoV-2 using amplicon-based sequencing of clinical samples.

Genotyping of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been instrumental in monitoring viral evolution and transmission during the pandemic. The quality of the sequence data obtained from these genotyping efforts depends on several factors, including the quantity/integrity of the input material, the technology, and laboratory-specific implementation. The current lack of guidelines for SARS-CoV-2 genotyping leads to inclusion of error-containing genome sequences in genomic epidemiology studies. We aimed to establish clear and broadly applicable recommendations for reliable virus genotyping. We established and used a sequencing data analysis workflow that reliably identifies and removes technical artefacts; such artefacts can result in miscalls when using alternative pipelines to process clinical samples and synthetic viral genomes with an amplicon-based genotyping approach. We evaluated the impact of experimental factors, including viral load and sequencing depth, on correct sequence determination. We found that at least 1000 viral genomes are necessary to confidently detect variants in the SARS-CoV-2 genome at frequencies of ≥10%. The broad applicability of our recommendations was validated in over 200 clinical samples from six independent laboratories. The genotypes we determined for clinical isolates with sufficient quality cluster by sampling location and period. Our analysis also supports the rise in frequencies of 20A.EU1 and 20A.EU2, two recently reported European strains whose dissemination was facilitated by travel during the summer of 2020. We present much-needed recommendations for the reliable determination of SARS-CoV-2 genome sequences and demonstrate their broad applicability in a large cohort of clinical samples

iPSC modeling of RBM20-deficient DCM identifies upregulation of RBM20 as a therapeutic strategy

Recent advances in induced pluripotent stem cell (iPSC) technology and directed differentiation of iPSCs into cardiomyocytes (iPSC-CMs) make it possible to model genetic heart disease in vitro. We apply CRISPR/Cas9 genome editing technology to introduce three RBM20 mutations in iPSCs and differentiate them into iPSC-CMs to establish an in vitro model of RBM20 mutant dilated cardiomyopathy (DCM). In iPSC-CMs harboring a known causal RBM20 variant, the splicing of RBM20 target genes, calcium handling, and contractility are impaired consistent with the disease manifestation in patients. A variant (Pro633Leu) identified by exome sequencing of patient genomes displays the same disease phenotypes, thus establishing this variant as disease causing. We find that all-trans retinoic acid upregulates RBM20 expression and reverts the splicing, calcium handling, and contractility defects in iPSC-CMs with different causal RBM20 mutations. These results suggest that pharmacological upregulation of RBM20 expression is a promising therapeutic strategy for DCM patients with a heterozygous mutation in RBM20

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

Background: Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. Methods: The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model—a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates—with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality—which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. Findings: The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2–100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1–290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1–211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4–48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3–37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7–9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. Interpretation: Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. Funding: Bill & Melinda Gates Foundation

Silencing of genes and alleles by RNAi in <em>Anopheles gambiae</em>.

Anopheles gambiae mosquitoes are the major vectors of human malaria parasites. However, mosquitoes are not passive hosts for parasites, actively limiting their development in vivo. Our current understanding of the mosquito antiparasitic response is mostly based on the phenotypic analysis of gene knockdowns obtained by RNA interference (RNAi), through the injection or transfection of long dsRNAs in adult mosquitoes or cultured cells, respectively. Recently, RNAi has been extended to silence specifically one allele of a given gene in a heterozygous context, thus allowing to compare the contribution of different alleles to a phenotype in the same genetic background

Dissecting the genetic basis of resistance to malaria parasites in Anopheles gambiae.

The ability of Anopheles gambiae mosquitoes to transmit Plasmodium parasites is highly variable between individuals. However, the genetic basis of this variability has remained unknown. We combined genome-wide mapping and reciprocal allele-specific RNA interference (rasRNAi) to identify the genomic locus that confers resistance to malaria parasites and demonstrated that polymorphisms in a single gene encoding the antiparasitic thioester-containing protein 1 (TEP1) explain a substantial part of the variability in parasite killing. The link between TEP1 alleles and resistance to malaria may offer new tools for controlling malaria transmission. The successful application of rasRNAi in Anopheles suggests that it could also be applied to other organisms where RNAi is feasible to dissect complex phenotypes to the level of individual quantitative trait alleles